OpenRaiser/Intern-Atlas huggingface dataset:
PVNet: Pixel-wise Voting Network for 6DoF Pose Estimation
Venue: arXiv cs.CV 2018
Year: 2018

This paper addresses the challenge of 6DoF pose estimation from a single RGB image under severe occlusion or truncation. Many recent works have shown that a two-stage approach, which first detects keypoints and then solves a Perspective-n-Point (PnP) problem for pose estimation, achieves remarkable performance. However, most of these methods only localize a set of sparse keypoints by regressing their image coordinates or heatmaps, which are sensitive to occlusion and truncation. Instead, we introduce a Pixel-wise Voting Network (PVNet) to regress pixel-wise unit vectors pointing to the keypoints and use these vectors to vote for keypoint locations using RANSAC. This creates a flexible representation for localizing occluded or truncated keypoints. Another important feature of this representation is that it provides uncertainties of keypoint locations that can be further leveraged by the PnP solver. Experiments show that the proposed approach outperforms the state of the art on the LINEMOD, Occlusion LINEMOD and YCB-Video datasets by a large margin, while being efficient for real-time pose estimation. We further create a Truncation LINEMOD dataset to validate the robustness of our approach against truncation. The code will be avaliable at https://zju-3dv.github.io/pvnet/.
DC-Bias and Power Allocation in Cooperative VLC Networks for Joint Information and Energy Transfer
Venue: arXiv cs.IT 2018
Year: 2018

Visible light communications (VLC) have emerged as strong candidates for meeting the escalating demand for high data rates. Consider a VLC network, where multiple access-points (APs) serve both energy-harvesting users (EHUs), i.e., users which harvest energy from light intensity, and information-users (IUs), i.e., users which gather data information. In order to jointly balance the achievable sum-rate at the IUs and the energy harvested by the EHUs, the paper considers maximizing a network-wide utility, which consists of a weighted-sum of the IUs sum-rate and the EHUs harvested energy, subject to individual IU rate constraint, individual EHU harvested-energy constraint, and AP power constraints, so as to jointly determine the direct current (DC)-bias value at each AP, and the users powers. The paper solves such a difficult non-convex optimization problem using an iterative approach which relies on inner convex approximations, and compensates for the used approximations using proper outer-loop updates. The paper further considers solving the special cases of the problem, i.e., maximizing the sum-rate, and maximizing the total harvested-energy, both subject to the same constraints. Numerical results highlight the significant performance improvement of the proposed algorithms, and illustrate the impacts of the network parameters on the performance trade-off between the sum-rate and harvested-energy.
Task Elimination may Actually Increase Throughput Time
Venue: arXiv cs.DC 2018
Year: 2018

The well-known Task Elimination redesign principle suggests to remove unnecessary tasks from a process to improve on time and cost. Although there seems to be a general consensus that removing work can only improve the throughput time of the process, this paper shows that this is not necessarily the case by providing an example that uses plain M/M/c activities. This paper also shows that the Task Automation and Parallelism redesign principles may also lead to longer throughput times. Finally, apart from these negative results, the paper also show under which assumption these redesign principles indeed can only improve the throughput time.
Multi-Player Multi-Armed Bandit Based Resource Allocation for D2D Communications
Venue: arXiv cs.NI 2018
Year: 2018

Device-to-device (D2D) communications is expected to play a significant role in increasing the system capacity of the fifth generation (5G) wireless networks. To accomplish this, efficient power and resource allocation algorithms need to be devised for the D2D users. Since the D2D users are treated as secondary users, their interference to the cellular users (CUs) should not hamper the CU communications. Most of the prior works on D2D resource allocation assume full channel state information (CSI) at the base station (BS). However, the required channel gains for the D2D pairs may not be known. To acquire these in a fast fading channel requires extra power and control overhead. In this paper, we assume partial CSI and formulate the D2D power and resource allocation problem as a multi-armed bandit problem. We propose a power allocation scheme for the D2D users in which the BS allocates power to the D2D users if a certain signal-to-interference-plus-noise ratio (SINR) is maintained for the CUs. In a single player environment a D2D user selects a CU in every time slot by employing UCB1 algorithm. Since this resource allocation problem can also be considered as an adversarial bandit problem we have applied the exponential-weight algorithm for exploration and exploitation (Exp3) to solve it. In a multiple player environment, we extend UCB1 and Exp3 to multiple D2D users. We also propose two algorithms that are based on distributed learning algorithm with fairness (DLF) and kth-UCB1 algorithms in which the D2D users are ranked. Our simulation results show that our proposed algorithms are fair and achieve good performance.
RF Jamming Classification using Relative Speed Estimation in Vehicular Wireless Networks
Venue: arXiv cs.CR 2018
Year: 2018

Wireless communications are vulnerable against radio frequency (RF) jamming which might be caused either intentionally or unintentionally. A particular subset of wireless networks, vehicular ad-hoc networks (VANET) which incorporate a series of safety-critical applications, may be a potential target of RF jamming with detrimental safety effects. To ensure secure communication and defend it against this type of attacks, an accurate detection scheme must be adopted. In this paper we introduce a detection scheme that is based on supervised learning. The machine-learning algorithms, KNearest Neighbors (KNN) and Random Forests (RF), utilize a series of features among which is the metric of the variations of relative speed (VRS) between the jammer and the receiver that is passively estimated from the combined value of the useful and the jamming signal at the receiver. To the best of our knowledge, this metric has never been utilized before in a machine-learning detection scheme in the literature. Through offline training and the proposed KNN-VRS, RF-VRS classification algorithms, we are able to efficiently detect various cases of Denial of Service Attacks (DoS) jamming attacks, differentiate them from cases of interference as well as foresee a potential danger successfully and act accordingly.
Developing Cyber Buffer Zones
Venue: arXiv cs.CY 2018
Year: 2018

The United Nations conducts peace operations around the world, aiming tomaintain peace and security in conflict torn areas. Whilst early operations werelargely successful, the changing nature of warfare and conflict has often left peaceoperations strugglingto adapt. In this article, we make a contribution towardsefforts to plan for the next evolution in both intra and inter-state conflict: cyberwarfare. It is now widely accepted that cyber warfare will be a component offuture conflicts, and much researchhas been devoted to how governments andmilitaries can prepare for and fight in this new domain [1]. Despite the vastamount of research relating to cyber warfare, there has been less discussion onits impact towards successful peace operations. This is agap in knowledge thatis important to address, since the restoration of peace following conflict of anykind is of global importance. It is however a complex topic requiring discussionacross multiple domains. Input from the technical, political, governmental andsocietal domains are critical in forming the concept of cyber peacekeeping.Previous work on this topic has sought to define the concept of cyber peacekeeping[2, 3, 4]. We build upon this work by exploring the practicalities ofstarting up a cyber peacekeeping component and setting up a Cyber Buffer Zone (CBZ).
Large-Scale Object Detection of Images from Network Cameras in Variable Ambient Lighting Conditions
Venue: arXiv cs.CV 2018
Year: 2018

Computer vision relies on labeled datasets for training and evaluation in detecting and recognizing objects. The popular computer vision program, YOLO ("You Only Look Once"), has been shown to accurately detect objects in many major image datasets. However, the images found in those datasets, are independent of one another and cannot be used to test YOLO's consistency at detecting the same object as its environment (e.g. ambient lighting) changes. This paper describes a novel effort to evaluate YOLO's consistency for large-scale applications. It does so by working (a) at large scale and (b) by using consecutive images from a curated network of public video cameras deployed in a variety of real-world situations, including traffic intersections, national parks, shopping malls, university campuses, etc. We specifically examine YOLO's ability to detect objects in different scenarios (e.g., daytime vs. night), leveraging the cameras' ability to rapidly retrieve many successive images for evaluating detection consistency. Using our camera network and advanced computing resources (supercomputers), we analyzed more than 5 million images captured by 140 network cameras in 24 hours. Compared with labels marked by humans (considered as "ground truth"), YOLO struggles to consistently detect the same humans and cars as their positions change from one frame to the next; it also struggles to detect objects at night time. Our findings suggest that state-of-the art vision solutions should be trained by data from network camera with contextual information before they can be deployed in applications that demand high consistency on object detection.
The role of visual saliency in the automation of seismic interpretation
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, we propose a workflow based on SalSi for the detection and delineation of geological structures such as salt domes. SalSi is a seismic attribute designed based on the modeling of human visual system that detects the salient features and captures the spatial correlation within seismic volumes for delineating seismic structures. Using SalSi, we can not only highlight the neighboring regions of salt domes to assist a seismic interpreter but also delineate such structures using a region growing method and post-processing. The proposed delineation workflow detects the salt-dome boundary with very good precision and accuracy. Experimental results show the effectiveness of the proposed workflow on a real seismic dataset acquired from the North Sea, F3 block. For the subjective evaluation of the results of different salt-dome delineation algorithms, we have used a reference salt-dome boundary interpreted by a geophysicist. For the objective evaluation of results, we have used five different metrics based on pixels, shape, and curvedness to establish the effectiveness of the proposed workflow. The proposed workflow is not only fast but also yields better results as compared to other salt-dome delineation algorithms and shows a promising potential in seismic interpretation.
Characterizing Long-Running Political Phenomena on Social Media
Venue: arXiv cs.CY 2018
Year: 2018

Social media provides many opportunities to monitor and evaluate political phenomena such as referendums and elections. In this study, we propose a set of approaches to analyze long-running political events on social media with a real-world experiment: the debate about Brexit, i.e., the process through which the United Kingdom activated the option of leaving the European Union. We address the following research questions: Could Twitter-based stance classification be used to demonstrate public stance with respect to political events? What is the most efficient and comprehensive approach to measuring the impact of politicians on social media? Which of the polarized sides of the debate is more responsive to politician messages and the main issues of the Brexit process? What is the share of bot accounts in the Brexit discussion and which side are they for? By combining the user stance classification, topic discovery, sentiment analysis, and bot detection, we show that it is possible to obtain useful insights about political phenomena from social media data. We are able to detect relevant topics in the discussions, such as the demand for a new referendum, and to understand the position of social media users with respect to the different topics in the debate. Our comparative and temporal analysis of political accounts can detect the critical periods of the Brexit process and the impact they have on the debate.
Multi-task Prediction of Patient Workload
Venue: arXiv cs.CY 2018
Year: 2018

Developing reliable workload predictive models can affect many aspects of clinical decision making procedure. The primary challenge in healthcare systems is handling the demand uncertainty over the time. This issue becomes more critical for the healthcare facilities that provide service for chronic disease treatment because of the need for continuous treatments over the time. Although some researchers focused on exploring the methods for workload prediction recently, few types of research mainly focused on forecasting a quantitative measure for the workload of healthcare providers. Also, among the mentioned studies most of them just focused on workload prediction within one facility. The drawback of the previous studies is the problem is not investigated for multiple facilities where the quality of provided service, the equipment, and resources used for provided service as well as the diagnosis and treatment procedures may differ even for patients with similar conditions. To tackle the mentioned issue, this paper suggests a framework for patient workload prediction by using patients data from VA facilities across the US. To capture the information of patients with similar attributes and make the prediction more accurate, a heuristic cluster based algorithm for single task learning as well as a multi task learning approach are developed in this research.
Proceedings of the 2018 XCSP3 Competition
Venue: arXiv cs.AI 2018
Year: 2018

This document represents the proceedings of the 2018 XCSP3 Competition. The results of this competition of constraint solvers were presented at CP'18, the 24th International Conference on Principles and Practice of Constraint Programming, held in Lille, France from 27th August 2018 to 31th August, 2018.
AIDA: Associative DNN Inference Accelerator
Venue: arXiv cs.DC 2018
Year: 2018

We propose AIDA, an inference engine for accelerating fully-connected (FC) layers of Deep Neural Network (DNN). AIDA is an associative in-memory processor, where the bulk of data never leaves the confines of the memory arrays, and processing is performed in-situ. AIDA area and energy efficiency strongly benefit from sparsity and lower arithmetic precision. We show that AIDA outperforms the state of art inference accelerator, EIE, by 14.5x (peak performance) and 2.5x (throughput).
PI-Edge: A Low-Power Edge Computing System for Real-Time Autonomous Driving Services
Venue: arXiv cs.DC 2018
Year: 2018

To simultaneously enable multiple autonomous driving services on affordable embedded systems, we designed and implemented π-Edge, a complete edge computing framework for autonomous robots and vehicles. The contributions of this paper are three-folds: first, we developed a runtime layer to fully utilize the heterogeneous computing resources of low-power edge computing systems; second, we developed an extremely lightweight operating system to manage multiple autonomous driving services and their communications; third, we developed an edge-cloud coordinator to dynamically offload tasks to the cloud to optimize client system energy consumption. To the best of our knowledge, this is the first complete edge computing system of a production autonomous vehicle. In addition, we successfully implemented π-Edge on a Nvidia Jetson and demonstrated that we could successfully support multiple autonomous driving services with only 11 W of power consumption, and hence proving the effectiveness of the proposed π-Edge system.
Tight Bounds on the Minimum Size of a Dynamic Monopoly
Venue: arXiv cs.DS 2018
Year: 2018

Assume that you are given a graph $G=(V,E)$ with an initial coloring, where each node is black or white. Then, in discrete-time rounds all nodes simultaneously update their color following a predefined deterministic rule. This process is called two-way $r$-bootstrap percolation, for some integer $r$, if a node with at least $r$ black neighbors gets black and white otherwise. Similarly, in two-way $α$-bootstrap percolation, for some $0<α<1$, a node gets black if at least $α$ fraction of its neighbors are black, and white otherwise. The two aforementioned processes are called respectively $r$-bootstrap and $α$-bootstrap percolation if we require that a black node stays black forever. For each of these processes, we say a node set $D$ is a dynamic monopoly whenever the following holds: If all nodes in $D$ are black then the graph gets fully black eventually. We provide tight upper and lower bounds on the minimum size of a dynamic monopoly.
Swarm Intelligent Algorithm For Re-entrant Hybrid Flow shop Scheduling Problems
Venue: arXiv cs.MA 2018
Year: 2018

In order to solve Re-entrant Hybrid Flowshop (RHFS) scheduling problems and establish simulations and processing models, this paper uses Wolf Pack Algorithm (WPA) as global optimization. For local assignment, it takes minimum remaining time rule. Scouting behaviors of wolf are changed in former optimization by means of levy flight, extending searching ranges and increasing rapidity of convergence. When it comes to local extremum of WPA, dynamic regenerating individuals with high similarity adds diversity. Hanming distance is used to judge individual similarity for increased quality of individuals, enhanced search performance of the algorithm in solution space and promoted evolutionary vitality.A painting workshop in a bus manufacture enterprise owns typical features of re-entrant hybrid flowshop. Regarding it as the algorithm applied target, this paper focus on resolving this problem with LDWPA (Dynamic wolf pack algorithm based on Levy Flight). Results show that LDWPA can solve re-entrant hybrid flowshop scheduling problems effectively.
Automatic Inspection of Utility Scale Solar Power Plants using Deep Learning
Venue: arXiv cs.LG 2018
Year: 2018

Solar energy has the potential to become the backbone energy source for the world. Utility scale solar power plants (more than 50 MW) could have more than 100K individual solar modules and be spread over more than 200 acres of land. Traditionally methods of monitoring each module become too costly in the utility scale. We demonstrate an alternative using the recent advances in deep learning to automatically analyze drone footage. We show that this can be a quick and reliable alternative. We show that it can save huge amounts of power and the impact the developing world hugely.
Genetic-Gated Networks for Deep Reinforcement
Venue: arXiv cs.NE 2018
Year: 2018

We introduce the Genetic-Gated Networks (G2Ns), simple neural networks that combine a gate vector composed of binary genetic genes in the hidden layer(s) of networks. Our method can take both advantages of gradient-free optimization and gradient-based optimization methods, of which the former is effective for problems with multiple local minima, while the latter can quickly find local minima. In addition, multiple chromosomes can define different models, making it easy to construct multiple models and can be effectively applied to problems that require multiple models. We show that this G2N can be applied to typical reinforcement learning algorithms to achieve a large improvement in sample efficiency and performance.
Scope of Research on Particle Swarm Optimization Based Data Clustering
Venue: arXiv cs.NE 2018
Year: 2018

Optimization is nothing but a mathematical technique which finds maxima or minima of any function of concern in some realistic region. Different optimization techniques are proposed which are competing for the best solution. Particle Swarm Optimization (PSO) is a new, advanced, and most powerful optimization methodology that performs empirically well on several optimization problems. It is the extensively used Swarm Intelligence (SI) inspired optimization algorithm used for finding the global optimal solution in a multifaceted search region. Data clustering is one of the challenging real world applications that invite the eminent research works in variety of fields. Applicability of different PSO variants to data clustering is studied in the literature, and the analyzed research work shows that, PSO variants give poor results for multidimensional data. This paper describes the different challenges associated with multidimensional data clustering and scope of research on optimizing the clustering problems using PSO. We also propose a strategy to use hybrid PSO variant for clustering multidimensional numerical, text and image data.
Merkle's Key Agreement Protocol is Optimal: An $O(n^2)$ Attack on any Key Agreement from Random Oracles
Venue: arXiv cs.CC 2019
Year: 2019

We prove that every key agreement protocol in the random oracle model in which the honest users make at most $n$ queries to the oracle can be broken by an adversary who makes $O(n^2)$ queries to the oracle. This improves on the previous $\widetildeΩ(n^6)$ query attack given by Impagliazzo and Rudich (STOC '89) and resolves an open question posed by them. Our bound is optimal up to a constant factor since Merkle proposed a key agreement protocol in 1974 that can be easily implemented with $n$ queries to a random oracle and cannot be broken by any adversary who asks $o(n^2)$ queries.
Lower Bounds on Signatures from Symmetric Primitives
Venue: arXiv cs.CC 2019
Year: 2019

We show that every construction of one-time signature schemes from a random oracle achieves black-box security at most $2^{(1+o(1))q}$, where $q$ is the total number of oracle queries asked by the key generation, signing, and verification algorithms. That is, any such scheme can be broken with probability close to $1$ by a (computationally unbounded) adversary making $2^{(1+o(1))q}$ queries to the oracle. This is tight up to a constant factor in the number of queries, since a simple modification of Lamport's one-time signatures (Lamport '79) achieves $2^{(0.812-o(1))q}$ black-box security using $q$ queries to the oracle. Our result extends (with a loss of a constant factor in the number of queries) also to the random permutation and ideal-cipher oracles. Since the symmetric primitives (e.g. block ciphers, hash functions, and message authentication codes) can be constructed by a constant number of queries to the mentioned oracles, as corollary we get lower bounds on the efficiency of signature schemes from symmetric primitives when the construction is black-box. This can be taken as evidence of an inherent efficiency gap between signature schemes and symmetric primitives.